Seed models are trained only on the transcribed data for each language.
These seed models are used to decode the untranscribed data and produce pseudotranscripts.
We treat these pseudotranscripts as though they were ground truth transcripts and combine them with the transcribed data, training the model from scratch to produce our final semisupervised acoustic model.
It can be beneficial to do data selection on the pseudotranscripts [1]}, [2]}, but we keep all data in our experiments..
